This file contains regional models examining the functional relationship between agricultural landscape diversity and agricultural yields. This work applies hierachical Bayesian spatiotemporal modeling techniques to estimation of the effect of diversity of the agricultural landscape on the yield of corn, soy, and winter wheat.
REPRESENTING SPACE
The yield data available is at a county scale and the distribution of yields across spcae exhibits strong autocorrelation where yields in neighboring counties are more alike than yields in distant counties. This spatial autocorrelation is accounted for using a standard Conditional Autoreggressive dependency model based on adjacency for all counties in the conterminous US. In order to account for additional county-specific factors that contribute to yields a county iid random effect term is also included, yielding a Besag-York-Mollie (BYM) spatial dependency model. A seperate county adjacency matrix for each region-crop combination is created.
REGIONAL CORN MODELS
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
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Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ------------ ------------- ----------- ----------- -------------
(Intercept) 4.2142 0.0172 4.1804 4.2142 4.2479
PERC_IRR 0.0054 0.0007 0.0040 0.0054 0.0067
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 46.6381 0.9349 44.7878 46.6476 48.4551
Precision for TP 80.5151 38.9316 28.4105 72.8525 177.1899
Precision for SDD 36.6466 9.6399 21.4419 35.4000 59.0074
Precision for GDD 285.1326 123.7458 121.6174 259.3237 596.3728
Precision for SDI 273449.3095 2909257.6177 2681.9743 35116.2912 1774927.0746
Precision for CNTY 47.7249 3.2500 41.4817 47.6936 54.2345
Phi for CNTY 0.9968 0.0036 0.9874 0.9980 0.9999
Precision for AERCODE.id 526.9856 184.7079 267.6953 492.4909 981.8349
Precision for AERCODE.id2 10686.5281 3340.1745 5759.1103 10133.9753 18745.3029
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Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-5351.337 2603.654 0.0187517 0.7682262
Loading required package: viridis
Loading required package: viridisLite
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Cross Validation Diagnostic Metrics
| 0.7822243 |
0.0244935 |
0.7941849 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ----------- ----------- ----------- ----------- ------------
(Intercept) 4.2234 0.0187 4.1872 4.2232 4.2605
PERC_IRR 0.0054 0.0007 0.0041 0.0054 0.0067
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 46.6678 0.9335 44.8259 46.6742 48.4922
Precision for TP 81.7406 38.7751 28.3291 74.6530 177.4969
Precision for SDD 37.1012 9.5651 21.2329 36.1711 58.5881
Precision for GDD 289.7569 117.9846 123.4765 268.6063 579.7156
Precision for SIDI 24376.1951 55243.1038 1562.1032 10434.9888 133757.7083
Precision for CNTY 47.6212 3.2674 41.7159 47.4261 54.5594
Phi for CNTY 0.9968 0.0035 0.9875 0.9979 0.9999
Precision for AERCODE.id 527.7686 181.3298 265.3790 496.4499 969.5521
Precision for AERCODE.id2 10978.4700 3306.4277 5889.8534 10511.4919 18771.4041
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Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- ----- ---------- ----------
-5351.212 2604 0.0187377 0.7683414
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Cross Validation Diagnostic Metrics
| 0.7964911 |
0.0221084 |
0.7734521 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ------------ ------------- ----------- ----------- -------------
(Intercept) 4.2187 0.0174 4.1846 4.2187 4.2529
PERC_IRR 0.0054 0.0007 0.0040 0.0054 0.0067
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 47.6056 0.7046 46.7198 47.4576 49.3055
Precision for TP 81.8447 38.7862 28.4100 74.7553 177.6123
Precision for SDD 36.8643 9.6546 21.5116 35.6539 59.2073
Precision for GDD 303.4041 119.1342 126.4317 285.5043 587.9036
Precision for RICH 196116.5380 1340528.2409 3496.6615 36394.2116 1289586.3896
Precision for CNTY 46.0678 0.7659 44.2480 46.2179 47.0624
Phi for CNTY 1.0000 0.0000 1.0000 1.0000 1.0000
Precision for AERCODE.id 532.3527 177.8527 265.4198 505.2683 957.3958
Precision for AERCODE.id2 10722.9675 3269.6746 5755.5650 10238.2459 18464.6578
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Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-5355.083 2605.403 0.0187545 0.7682742
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Cross Validation Diagnostic Metrics
| 0.671935 |
0.0278649 |
0.7550168 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ---------- ---------- ----------- ---------- -----------
(Intercept) 4.2862 0.0297 4.2276 4.2863 4.3444
PERC_IRR 0.0113 0.0012 0.0089 0.0113 0.0138
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 19.8097 0.5188 18.8390 19.7903 20.8798
Precision for TP 369.6189 275.6383 68.0318 299.4925 1092.1615
Precision for SDD 23.1127 6.1267 12.7621 22.6054 36.6196
Precision for GDD 127.0646 64.9522 43.4414 113.2748 291.9393
Precision for SDI 1101.0559 1043.0219 190.8537 795.7093 3846.2163
Precision for CNTY 14.5350 1.3744 12.0994 14.4355 17.5011
Phi for CNTY 0.9780 0.0158 0.9371 0.9817 0.9968
Precision for AERCODE.id 138.8408 46.4999 69.5950 131.5660 250.1333
Precision for AERCODE.id2 4722.7082 1538.8123 2368.6858 4504.2470 8358.7737
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Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- ---------- --------- ---------
-12.79171 -58.85196 0.043167 0.651193
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Cross Validation Diagnostic Metrics
| 0.6500659 |
0.0693833 |
0.6184632 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ---------- ---------- ----------- ---------- -----------
(Intercept) 4.2592 0.0313 4.1973 4.2593 4.3202
PERC_IRR 0.0116 0.0012 0.0092 0.0116 0.0140
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 19.8277 0.5207 18.8518 19.8085 20.9008
Precision for TP 362.4075 300.2303 69.5720 278.6169 1153.9014
Precision for SDD 22.9556 6.3069 13.2752 22.0293 37.8919
Precision for GDD 136.5472 72.0356 45.3191 120.8523 320.2600
Precision for SIDI 890.2584 660.6546 184.5222 717.5824 2630.0188
Precision for CNTY 15.0778 1.4788 12.4647 14.9714 18.2573
Phi for CNTY 0.9733 0.0183 0.9264 0.9775 0.9955
Precision for AERCODE.id 132.2511 45.1442 67.9709 124.0851 242.3927
Precision for AERCODE.id2 4281.3214 1489.8991 2200.5885 3999.0802 7964.2306
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Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- ---------- ---------- ----------
-15.66584 -56.41943 0.0432211 0.6515934
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Cross Validation Diagnostic Metrics
| 0.6657347 |
0.0693209 |
0.5699762 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ---------- ---------- ----------- ---------- -----------
(Intercept) 4.3123 0.0295 4.2543 4.3124 4.3701
PERC_IRR 0.0111 0.0012 0.0087 0.0111 0.0135
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 19.9199 0.5225 18.9004 19.9174 20.9579
Precision for TP 374.3279 280.4680 71.5901 301.8961 1111.5748
Precision for SDD 23.1228 6.3948 13.3762 22.1592 38.3178
Precision for GDD 140.7063 73.4752 46.6680 124.9884 327.4825
Precision for RICH 927.8370 547.1467 275.0302 798.5071 2337.5207
Precision for CNTY 15.4579 1.5725 12.4538 15.4407 18.6223
Phi for CNTY 0.9655 0.0227 0.9087 0.9703 0.9943
Precision for AERCODE.id 135.7763 44.4110 68.2123 129.3153 241.1614
Precision for AERCODE.id2 4413.2071 1431.6697 2230.2598 4206.3938 7806.6522
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Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- ---------- ---------- ----------
-26.53221 -51.76297 0.0429573 0.6521698
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Cross Validation Diagnostic Metrics
| 0.6625002 |
0.0577074 |
0.6652018 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ------------ ------------- ----------- ----------- ------------
(Intercept) 4.5307 0.0241 4.4831 4.5308 4.5778
PERC_IRR 0.0304 0.0121 0.0069 0.0304 0.0544
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 68.9226 4.2583 60.8439 68.8227 77.6088
Precision for TP 480.3782 324.2380 112.5375 400.1823 1321.0250
Precision for SDD 141.7108 60.8638 57.7704 130.2808 292.5995
Precision for GDD 126729.4824 1548440.3412 877.6150 14109.9803 814105.9186
Precision for SDI 63539.2527 657295.0844 637.1940 8330.8652 413642.8831
Precision for CNTY 109.5745 26.8216 65.3538 106.8286 170.0906
Phi for CNTY 0.8090 0.1582 0.4009 0.8535 0.9874
Precision for AERCODE.id 19905.3254 18273.5085 2219.1684 14760.7599 68630.2886
Precision for AERCODE.id2 80652.0823 33059.0718 32244.3829 75473.8121 160148.8347
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Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-843.6714 409.5285 0.0121709 0.4902653
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Cross Validation Diagnostic Metrics
| 0.3740918 |
0.0186073 |
0.370263 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ------------ ------------- ----------- ----------- ------------
(Intercept) 4.5349 0.0241 4.4877 4.5348 4.5825
PERC_IRR 0.0302 0.0121 0.0067 0.0302 0.0541
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 68.8561 4.2492 60.8466 68.7367 77.5696
Precision for TP 482.4479 323.4648 112.8373 403.2390 1319.0566
Precision for SDD 142.1618 60.9802 58.3107 130.6291 293.4168
Precision for GDD 124352.5624 1415065.5894 892.0100 14568.9930 807937.8665
Precision for SIDI 154533.7301 2126483.7119 614.5955 14608.8256 992241.6155
Precision for CNTY 108.6734 26.5511 65.7622 105.5538 169.4989
Phi for CNTY 0.8085 0.1569 0.4039 0.8525 0.9860
Precision for AERCODE.id 19539.1995 18422.0983 2233.5206 14294.4051 68839.0652
Precision for AERCODE.id2 80784.9203 33418.2548 32520.2030 75304.7852 161657.5236
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Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-843.4376 409.2088 0.0121998 0.4900502
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Cross Validation Diagnostic Metrics
| 0.6085819 |
0.0150998 |
0.4229685 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ----------- ------------ ----------- ----------- ------------
(Intercept) 4.5331 0.0232 4.4874 4.5332 4.5785
PERC_IRR 0.0301 0.0120 0.0066 0.0301 0.0540
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 68.8190 4.2516 60.7831 68.7074 77.5203
Precision for TP 479.6983 325.3207 112.4885 398.7137 1326.2547
Precision for SDD 142.6346 61.3090 58.6101 130.9584 294.7995
Precision for GDD 50341.5955 271675.7196 610.6359 10366.0334 337266.0891
Precision for RICH 49117.9100 210139.9035 689.8862 12047.3893 324105.6568
Precision for CNTY 109.3419 26.6873 65.2932 106.6325 169.4767
Phi for CNTY 0.8110 0.1566 0.4067 0.8549 0.9876
Precision for AERCODE.id 19711.0277 18164.5883 2181.6094 14588.0545 68098.9763
Precision for AERCODE.id2 81135.1988 33809.4586 32954.8503 75348.8264 163123.5745
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Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-843.4964 409.3231 0.0121984 0.4896312
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Cross Validation Diagnostic Metrics
| 0.3874911 |
0.0211235 |
0.4451655 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ----------- ----------- ----------- ----------- ------------
(Intercept) 4.7234 0.0652 4.5986 4.7222 4.8546
PERC_IRR 0.0144 0.0030 0.0084 0.0145 0.0202
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 42.7950 4.5498 34.3628 42.6394 52.1872
Precision for TP 636.6272 1053.9992 45.0376 333.3069 3113.3750
Precision for SDD 189.5203 142.0761 42.0396 151.3506 561.8880
Precision for GDD 1170.5365 2482.2251 72.5194 521.2839 6310.6199
Precision for SDI 2013.2868 7623.2833 68.8215 589.6933 12603.5785
Precision for CNTY 11.1257 2.3513 7.1539 10.9106 16.3661
Phi for CNTY 0.4917 0.1668 0.1760 0.4936 0.8023
Precision for AERCODE.id 19110.2563 18812.8186 1484.0891 13621.2283 68789.4449
Precision for AERCODE.id2 45730.3058 25497.2403 13450.1910 40267.5243 110535.6248
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Table: Model Diagnostic Metrics
DIC CPO MSE R2
--------- --------- ---------- ----------
-166.502 61.97449 0.0162994 0.8047957
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Cross Validation Diagnostic Metrics
| 0.7795008 |
0.0346998 |
0.8047987 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ----------- ----------- ----------- ----------- ------------
(Intercept) 4.7145 0.0587 4.6008 4.7138 4.8316
PERC_IRR 0.0128 0.0032 0.0065 0.0129 0.0190
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 41.7821 4.3646 33.7718 41.5883 50.9417
Precision for TP 618.8255 948.7052 44.5808 339.8249 2926.9039
Precision for SDD 232.1437 192.7070 47.0864 177.7403 743.2756
Precision for GDD 3227.6410 13591.1091 103.5062 875.7718 20380.8610
Precision for SIDI 412.7495 592.1571 32.4183 236.6736 1879.9395
Precision for CNTY 11.6027 2.4388 7.5016 11.3704 17.0634
Phi for CNTY 0.5459 0.1670 0.2173 0.5517 0.8441
Precision for AERCODE.id 20725.8742 19798.0209 1805.3359 15042.2780 73456.7466
Precision for AERCODE.id2 48767.9135 26218.2604 14897.2504 43343.7629 115008.5543
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Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-165.2358 63.11838 0.0167083 0.8032014
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Cross Validation Diagnostic Metrics
| 0.8485234 |
0.0357488 |
0.7818907 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ----------- ----------- ----------- ----------- ------------
(Intercept) 4.7872 0.0764 4.6412 4.7863 4.9388
PERC_IRR 0.0116 0.0034 0.0050 0.0116 0.0181
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 42.2996 4.4303 34.1639 42.1051 51.5914
Precision for TP 453.7813 568.9475 42.0697 282.9200 1918.9353
Precision for SDD 216.4446 170.2710 45.3111 169.6801 665.1345
Precision for GDD 3892.7117 19197.2501 110.3369 941.9450 25002.0731
Precision for RICH 371.9123 437.9684 52.3493 240.5930 1489.4494
Precision for CNTY 11.6800 2.4662 7.5442 11.4406 17.2126
Phi for CNTY 0.5628 0.1631 0.2329 0.5728 0.8464
Precision for AERCODE.id 24785.4514 26818.3102 2114.6817 16791.7251 95817.5010
Precision for AERCODE.id2 48029.9805 25485.6506 14320.2083 43013.2600 111582.1278
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Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-165.6465 62.95575 0.0163854 0.8038771
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Cross Validation Diagnostic Metrics
| 0.9053707 |
0.0570671 |
0.5103345 |
2 |

REGIONAL SOY MODELS
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ---------- ---------- ----------- ---------- -----------
(Intercept) 3.3511 0.0152 3.3213 3.3511 3.3808
PERC_IRR 0.0049 0.0006 0.0037 0.0049 0.0060
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 77.0549 1.6057 73.9227 77.0461 80.2463
Precision for TP 489.4658 431.3383 93.6094 365.2248 1626.4020
Precision for SDD 87.0945 25.2817 48.0979 83.5560 146.5130
Precision for GDD 246.3878 100.2429 103.3409 229.1061 490.6404
Precision for SDI 1836.2670 1037.8458 530.9151 1611.2174 4458.2925
Precision for CNTY 60.7216 4.2216 52.9030 60.5587 69.4731
Phi for CNTY 0.9972 0.0035 0.9875 0.9984 1.0000
Precision for AERCODE.id 268.7808 76.2214 146.9085 259.7928 443.3791
Precision for AERCODE.id2 6488.5355 1788.6567 3636.5519 6269.3960 10624.1607
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Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-7881.263 3828.274 0.0112339 0.8024213
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Cross Validation Diagnostic Metrics
| 0.773622 |
0.0156994 |
0.7815069 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ------------ ------------- ----------- ----------- -------------
(Intercept) 3.3516 0.0154 3.3215 3.3516 3.3818
PERC_IRR 0.0052 0.0006 0.0040 0.0052 0.0063
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 76.5530 1.6393 73.5521 76.4689 79.9804
Precision for TP 489.6890 517.0285 92.2873 334.0849 1826.3002
Precision for SDD 83.9492 25.1102 46.4091 79.9470 144.1970
Precision for GDD 240.3204 96.0015 90.9586 228.8686 460.6279
Precision for SIDI 331193.3723 2502330.3488 4171.6169 54916.9534 2190790.4132
Precision for CNTY 58.9527 4.4171 51.5269 58.4881 68.7498
Phi for CNTY 0.9980 0.0026 0.9910 0.9989 1.0000
Precision for AERCODE.id 267.8147 77.2990 134.8962 262.8321 434.2977
Precision for AERCODE.id2 6012.3177 1797.5836 3324.6093 5725.8407 10323.8887
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Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-7844.815 3811.576 0.0113053 0.8014569
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Cross Validation Diagnostic Metrics
| 0.7076951 |
0.0198243 |
0.7686717 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ------------ ------------ ----------- ----------- ------------
(Intercept) 3.3479 0.0153 3.3177 3.3479 3.3779
PERC_IRR 0.0052 0.0006 0.0040 0.0052 0.0063
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 76.6007 1.6017 73.4316 76.6177 79.7077
Precision for TP 482.0734 444.6789 93.7467 351.4962 1651.4410
Precision for SDD 84.7098 24.1640 46.1749 81.8042 140.2104
Precision for GDD 234.1006 94.2184 98.8615 218.1275 462.6958
Precision for RICH 109800.8474 640800.4146 2730.2873 23507.2965 714435.0448
Precision for CNTY 59.2449 4.0557 51.5475 59.1559 67.4991
Phi for CNTY 0.9974 0.0033 0.9885 0.9986 1.0000
Precision for AERCODE.id 262.0719 73.2632 143.3197 254.0177 428.7565
Precision for AERCODE.id2 6335.1449 1780.2342 3566.2203 6092.6510 10497.6984
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-7845.297 3811.898 0.0113026 0.8011916
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.764215 |
0.0166556 |
0.7871187 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ---------- ---------- ----------- ---------- -----------
(Intercept) 3.2516 0.0201 3.2122 3.2515 3.2912
PERC_IRR 0.0079 0.0011 0.0057 0.0079 0.0102
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 32.5486 0.9033 30.8377 32.5228 34.3905
Precision for TP 659.1634 504.6714 137.6350 523.2940 1986.7935
Precision for SDD 83.4114 24.8616 44.9844 79.9559 141.9145
Precision for GDD 389.5829 214.3140 119.6602 342.9701 935.4386
Precision for SDI 2398.0794 1993.0794 492.4664 1833.8770 7643.7093
Precision for CNTY 15.4742 1.6927 12.3017 15.4344 18.9438
Phi for CNTY 0.9671 0.0173 0.9252 0.9701 0.9914
Precision for AERCODE.id 202.7514 76.0025 93.1505 189.8009 387.3861
Precision for AERCODE.id2 8556.8143 3149.9192 3999.7800 8023.8770 16189.9118
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-1773.304 783.8237 0.0263707 0.6541006
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.6682249 |
0.0409116 |
0.5814491 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ---------- ---------- ----------- ---------- -----------
(Intercept) 3.2241 0.0213 3.1820 3.2242 3.2657
PERC_IRR 0.0084 0.0011 0.0062 0.0084 0.0107
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 32.5439 0.8966 30.8154 32.5322 34.3430
Precision for TP 650.4807 497.5825 132.2043 517.5282 1963.8826
Precision for SDD 83.0788 24.9668 44.9360 79.4695 142.0091
Precision for GDD 362.3420 200.3892 113.9314 317.3217 875.8177
Precision for SIDI 1954.6394 1774.8975 337.3591 1443.3084 6648.5265
Precision for CNTY 15.7081 1.7325 12.4621 15.6663 19.2628
Phi for CNTY 0.9675 0.0173 0.9261 0.9704 0.9918
Precision for AERCODE.id 178.7108 63.7643 83.6578 168.9331 331.2374
Precision for AERCODE.id2 7567.9576 2730.2159 3605.0875 7109.6133 14165.2974
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-1771.599 785.0175 0.0264286 0.6539069
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.5844853 |
0.0462006 |
0.5702695 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ---------- ---------- ----------- ---------- -----------
(Intercept) 3.2648 0.0201 3.2255 3.2648 3.3045
PERC_IRR 0.0083 0.0011 0.0061 0.0083 0.0105
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 32.7946 0.9087 31.0074 32.7989 34.5728
Precision for TP 639.7733 510.5112 134.2234 498.1367 1993.3586
Precision for SDD 82.9976 24.4424 42.9061 80.5758 138.0170
Precision for GDD 380.8714 206.7266 114.3952 337.8512 901.2940
Precision for RICH 1112.4344 736.9620 316.9518 917.8801 3051.3268
Precision for CNTY 15.4798 1.7073 12.4387 15.3656 19.1424
Phi for CNTY 0.9654 0.0179 0.9219 0.9686 0.9901
Precision for AERCODE.id 184.7192 66.3022 84.5485 175.1294 341.2241
Precision for AERCODE.id2 8131.1315 2873.1375 3763.5885 7724.0613 14895.5227
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-1785.302 789.4312 0.0261885 0.6555727
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.6708578 |
0.0349296 |
0.6770893 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ----------- ----------- ----------- ----------- ------------
(Intercept) 3.5601 0.0288 3.5037 3.5600 3.6170
PERC_IRR 0.0062 0.0086 -0.0106 0.0062 0.0231
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 73.5285 5.3409 63.4523 73.3865 84.4692
Precision for TP 648.3918 713.3188 90.5557 435.4605 2496.7883
Precision for SDD 123.1463 61.4829 43.4198 110.2142 278.6273
Precision for GDD 1063.4320 1062.8725 151.1732 751.6247 3842.9753
Precision for SDI 19330.6953 93539.0951 469.6689 4646.4268 125157.1814
Precision for CNTY 100.8677 28.7769 55.7033 97.0821 167.9791
Phi for CNTY 0.7518 0.1996 0.2612 0.8067 0.9849
Precision for AERCODE.id 13579.6362 25735.5934 1008.9124 6547.9241 69710.2825
Precision for AERCODE.id2 57806.1127 28766.8662 19981.6283 51936.6923 129952.6756
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-668.8126 318.0198 0.0111219 0.4941469
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.6756589 |
0.0185459 |
0.2089638 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ----------- ----------- ----------- ----------- ------------
(Intercept) 3.5421 0.0318 3.4787 3.5425 3.6035
PERC_IRR 0.0063 0.0083 -0.0100 0.0063 0.0227
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 74.5307 5.4902 64.2467 74.3555 85.8560
Precision for TP 633.1501 699.9104 88.9814 424.0805 2451.3873
Precision for SDD 117.7142 58.8526 42.4181 105.0265 266.4400
Precision for GDD 1454.7276 1729.4192 175.2000 937.0108 5879.5005
Precision for SIDI 5969.4887 27514.9356 109.4764 1435.0037 38828.9138
Precision for CNTY 94.2517 25.3091 53.4767 91.3268 152.3021
Phi for CNTY 0.7803 0.1830 0.3128 0.8334 0.9864
Precision for AERCODE.id 12769.1891 24553.9536 928.8708 6097.3785 66323.2036
Precision for AERCODE.id2 59328.2963 29482.4920 20431.7906 53358.4518 132943.9857
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-674.7631 320.3977 0.0108975 0.4996233
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.431855 |
0.017372 |
0.4336319 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ----------- ----------- ----------- ----------- ------------
(Intercept) 3.5491 0.0289 3.4924 3.5491 3.6058
PERC_IRR 0.0057 0.0086 -0.0111 0.0057 0.0226
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 77.7987 5.8658 66.8247 77.6065 89.9127
Precision for TP 638.0665 683.1682 89.0978 434.8378 2417.8846
Precision for SDD 109.1177 52.9034 40.3406 98.0047 242.1444
Precision for GDD 1450.3749 1710.1968 179.5254 938.4626 5821.1577
Precision for RICH 981.4053 1084.0829 165.8733 655.5880 3771.2430
Precision for CNTY 82.6632 21.2746 49.1055 79.8674 131.9407
Phi for CNTY 0.8166 0.1534 0.4130 0.8616 0.9859
Precision for AERCODE.id 11024.2823 20004.6415 734.7119 5438.3841 55754.1302
Precision for AERCODE.id2 60049.4567 29913.6688 20396.0700 54076.1592 134729.3246
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-686.0494 325.6193 0.0103897 0.5109601
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.3576449 |
0.0201557 |
0.3276189 |
2 |

REGIONAL WINTER WHEAT MODELS
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ---------- ---------- ----------- ---------- -----------
(Intercept) 3.9170 0.0177 3.8823 3.9170 3.9516
PERC_IRR 0.0044 0.0007 0.0030 0.0044 0.0058
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 40.6061 1.0829 38.4958 40.5999 42.7606
Precision for TP 177.4851 108.3737 50.8788 151.2346 457.3401
Precision for SDD 307.4415 148.2713 111.0211 277.4644 680.3222
Precision for GDD 881.7632 566.2082 243.2962 739.0115 2362.1181
Precision for SDI 3111.4131 3938.9488 397.1376 1933.9093 13049.3001
Precision for CNTY 35.9773 3.5055 29.7111 35.7479 43.4940
Phi for CNTY 0.9958 0.0046 0.9834 0.9972 0.9998
Precision for AERCODE.id 157.6080 50.4619 79.4532 150.8640 275.2732
Precision for AERCODE.id2 4462.6703 1363.5846 2352.8179 4274.6807 7670.9137
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-2614.601 1270.864 0.0213955 0.7052124
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.6827445 |
0.0321062 |
0.7070313 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ----------- ------------ ----------- ----------- ------------
(Intercept) 3.9283 0.0174 3.8946 3.9281 3.9631
PERC_IRR 0.0045 0.0007 0.0031 0.0045 0.0059
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 40.4519 1.0860 38.4117 40.4136 42.6858
Precision for TP 172.6210 106.3376 50.1747 146.3412 449.8994
Precision for SDD 317.8087 154.9366 113.5751 286.2056 708.1135
Precision for GDD 870.9885 570.3632 243.1515 723.2363 2367.3339
Precision for SIDI 79680.5972 436017.1906 1417.3301 17064.5860 524546.2194
Precision for CNTY 36.0509 3.7394 29.8057 35.6376 44.4402
Phi for CNTY 0.9954 0.0047 0.9830 0.9968 0.9998
Precision for AERCODE.id 150.9631 53.3388 76.3047 140.9454 282.4260
Precision for AERCODE.id2 4595.9493 1361.9321 2387.6437 4450.9149 7694.9411
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
--------- --------- ---------- ----------
-2605.13 1266.922 0.0214951 0.7047437
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.7140845 |
0.030918 |
0.6611112 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ---------- ---------- ----------- ---------- -----------
(Intercept) 3.9275 0.0176 3.8930 3.9274 3.9622
PERC_IRR 0.0044 0.0007 0.0030 0.0044 0.0058
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 40.4612 1.0790 38.4050 40.4376 42.6382
Precision for TP 177.0722 107.2298 50.6860 151.3779 454.2673
Precision for SDD 323.1476 159.4549 115.0831 289.9894 726.1629
Precision for GDD 883.3958 578.6991 246.1655 733.5533 2401.6732
Precision for RICH 4201.9034 4796.0082 579.2553 2766.5147 16600.2093
Precision for CNTY 36.7769 3.5876 30.0675 36.6803 44.1188
Phi for CNTY 0.9957 0.0047 0.9830 0.9971 0.9998
Precision for AERCODE.id 152.9836 51.5971 77.9096 144.1892 278.3894
Precision for AERCODE.id2 4488.5038 1349.8544 2373.6571 4312.8271 7633.8546
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-2611.032 1270.057 0.0214735 0.7046792
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.6750961 |
0.031759 |
0.6688041 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ---------- ---------- ----------- ---------- -----------
(Intercept) 3.4149 0.0223 3.3711 3.4149 3.4586
PERC_IRR 0.0082 0.0011 0.0060 0.0082 0.0105
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 23.9822 0.7083 22.6105 23.9749 25.3996
Precision for TP 87.3874 38.8511 33.2437 80.4188 182.4302
Precision for SDD 64.5991 18.8261 34.7822 62.2622 108.0890
Precision for GDD 302.1134 218.8343 82.4897 240.9459 881.3839
Precision for SDI 2514.0350 3284.5731 245.5074 1531.7012 10803.2916
Precision for CNTY 28.7350 3.5386 22.4686 28.4964 36.3426
Phi for CNTY 0.9576 0.0410 0.8448 0.9700 0.9963
Precision for AERCODE.id 28.1938 7.5541 15.9889 27.3316 45.4541
Precision for AERCODE.id2 1233.4299 335.2599 682.7868 1199.1327 1991.4658
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-608.3738 259.1074 0.0358235 0.7610683
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.8656774 |
0.0490918 |
0.7415549 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ---------- ---------- ----------- ---------- -----------
(Intercept) 3.4137 0.0247 3.3645 3.4139 3.4617
PERC_IRR 0.0083 0.0012 0.0061 0.0083 0.0106
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 23.9945 0.7084 22.6143 23.9906 25.4034
Precision for TP 88.7569 39.3769 33.4129 81.8420 185.0396
Precision for SDD 63.8100 18.4931 34.2097 61.6589 106.1652
Precision for GDD 312.4643 220.5891 85.1093 252.0573 896.4737
Precision for SIDI 2347.6035 3764.1767 224.3390 1266.2632 11154.7376
Precision for CNTY 28.1254 3.4472 21.5035 28.1152 35.0049
Phi for CNTY 0.9725 0.0268 0.9008 0.9808 0.9988
Precision for AERCODE.id 27.1506 7.3095 15.3806 26.2982 43.9157
Precision for AERCODE.id2 1164.3404 340.5801 650.9135 1111.4089 1978.4178
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
--------- --------- ---------- ----------
-611.411 261.3331 0.0357279 0.7613734
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.7867415 |
0.0496004 |
0.7294256 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ---------- ---------- ----------- ---------- -----------
(Intercept) 3.4074 0.0230 3.3620 3.4075 3.4522
PERC_IRR 0.0082 0.0011 0.0060 0.0082 0.0104
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 24.0298 0.7138 22.6241 24.0350 25.4228
Precision for TP 87.7558 39.0042 33.1217 80.8458 183.1775
Precision for SDD 64.1986 18.6833 34.7667 61.8016 107.6672
Precision for GDD 320.2854 224.8064 86.1243 259.3603 914.0443
Precision for RICH 3929.0164 5149.5697 492.4506 2394.0703 16784.3256
Precision for CNTY 28.1838 3.4787 21.5202 28.1659 35.1429
Phi for CNTY 0.9708 0.0283 0.8958 0.9796 0.9988
Precision for AERCODE.id 27.2509 7.4958 15.5522 26.2418 44.7396
Precision for AERCODE.id2 1214.9231 331.0162 679.6608 1177.9879 1966.8760
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
--------- --------- ---------- ----------
-611.938 261.6575 0.0356628 0.7614084
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.714093 |
0.0552462 |
0.7321951 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ----------- ------------ ----------- ----------- ------------
(Intercept) 3.8989 0.0279 3.8430 3.8993 3.9526
PERC_IRR 0.0131 0.0099 -0.0062 0.0131 0.0327
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 85.9081 7.5420 71.7963 85.6756 101.4425
Precision for TP 34917.3850 152883.7602 814.9333 8931.6461 225123.3940
Precision for SDD 2029.2295 3001.5359 206.3372 1149.7798 9287.6783
Precision for GDD 291.5210 147.5742 100.4794 260.4684 665.4212
Precision for SDI 2090.7468 2991.0976 180.6981 1204.4375 9483.1228
Precision for CNTY 90.7888 22.5324 54.3388 88.1639 142.4760
Phi for CNTY 0.3520 0.1963 0.0619 0.3224 0.7805
Precision for AERCODE.id 7415.8052 16391.5010 419.2259 3196.0686 40768.4247
Precision for AERCODE.id2 26727.8727 15519.3666 8084.9294 23091.0691 66719.7064
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- -------- ---------- ----------
-533.6433 257.783 0.0091701 0.6212439
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.4406014 |
0.0211711 |
0.5252146 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ----------- ------------ ----------- ----------- ------------
(Intercept) 3.9227 0.0252 3.8741 3.9225 3.9731
PERC_IRR 0.0104 0.0093 -0.0080 0.0104 0.0287
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 84.9419 7.4067 71.0811 84.7130 100.1980
Precision for TP 17223.5939 44183.7000 670.0129 6572.5926 100326.7087
Precision for SDD 2999.2483 5616.9626 236.3724 1461.9453 15324.4121
Precision for GDD 280.8333 142.0924 97.0395 250.8808 640.9328
Precision for SIDI 53414.0994 482901.9053 381.5683 7267.4675 355146.9300
Precision for CNTY 82.5343 20.1227 49.7289 80.2782 128.4888
Phi for CNTY 0.3776 0.1818 0.0824 0.3620 0.7537
Precision for AERCODE.id 10306.3874 27528.9320 402.6150 3828.2774 60696.8187
Precision for AERCODE.id2 28921.8609 16484.6946 8847.2268 25128.4367 71364.9426
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
--------- --------- ---------- ----------
-531.181 256.4403 0.0093379 0.6183205
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.5981288 |
0.0155635 |
0.6698219 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ----------- ----------- ----------- ----------- -----------
(Intercept) 3.9142 0.0242 3.8667 3.9142 3.9617
PERC_IRR 0.0109 0.0097 -0.0081 0.0109 0.0300
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 86.2391 7.6658 71.8671 86.0186 101.9578
Precision for TP 15131.8255 34172.5280 673.0431 6339.7215 83955.9023
Precision for SDD 2510.4012 4095.0356 222.5436 1336.5574 12065.9013
Precision for GDD 278.8893 140.9779 97.9555 248.7238 636.2787
Precision for RICH 10356.9579 45322.9461 287.2311 2702.7558 66405.4899
Precision for CNTY 82.8027 19.8757 50.2519 80.6254 128.0671
Phi for CNTY 0.3685 0.1822 0.0810 0.3483 0.7561
Precision for AERCODE.id 9408.9618 24179.3118 398.3579 3606.0822 54288.2564
Precision for AERCODE.id2 28976.3573 16190.7187 8814.5371 25380.3768 70402.0258
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-533.7965 257.4915 0.0091007 0.6215751
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.5209294 |
0.0153397 |
0.4925593 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ---------- ---------- ----------- ---------- -----------
(Intercept) 3.9848 0.0686 3.8529 3.9839 4.1223
PERC_IRR 0.0151 0.0026 0.0101 0.0151 0.0202
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 33.2167 1.9800 29.4569 33.1711 37.2527
Precision for TP 151.7446 96.4373 41.2759 127.8930 401.7231
Precision for SDD 560.1093 680.2609 80.6495 356.1705 2284.8543
Precision for GDD 266.7867 211.9980 56.6562 207.9632 828.8339
Precision for SDI 2570.0639 8503.7065 115.8524 842.5804 15612.0570
Precision for CNTY 8.4252 1.1653 6.3586 8.3486 10.9376
Phi for CNTY 0.2796 0.0779 0.1462 0.2733 0.4494
Precision for AERCODE.id 101.1539 43.4153 42.4556 92.5641 209.6318
Precision for AERCODE.id2 3230.8422 1315.6535 1416.8973 2980.2756 6493.6589
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-345.7382 124.6112 0.0224348 0.8536887
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.8779729 |
0.0385213 |
0.8328432 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ---------- ---------- ----------- ---------- -----------
(Intercept) 3.9028 0.0751 3.7561 3.9024 4.0513
PERC_IRR 0.0135 0.0024 0.0087 0.0135 0.0183
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 34.0616 2.0674 30.1020 34.0313 38.2231
Precision for TP 166.1384 111.7114 44.2473 137.0613 459.8257
Precision for SDD 795.9452 1182.2189 95.2220 451.6111 3607.2307
Precision for GDD 236.0298 169.8944 56.1832 190.9294 683.7322
Precision for SIDI 64.6477 87.2374 7.3970 38.7327 280.1396
Precision for CNTY 9.0385 1.2618 6.7871 8.9610 11.7467
Phi for CNTY 0.3260 0.0880 0.1725 0.3198 0.5144
Precision for AERCODE.id 99.7972 41.6958 41.7493 92.1054 202.6530
Precision for AERCODE.id2 3228.1650 1270.5250 1400.2791 3012.8177 6321.3133
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-364.8695 134.1699 0.0218895 0.8566682
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.9656443 |
0.0473564 |
0.7630333 |
2 |
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
[[1]]
Table: Summary Table of Model Estimates
mean sd 0.025quant 0.5quant 0.975quant
---------------------------------------- ---------- ---------- ----------- ---------- -----------
(Intercept) 4.0392 0.0687 3.9065 4.0384 4.1762
PERC_IRR 0.0134 0.0025 0.0084 0.0134 0.0184
ACRES 0.0000 0.0000 0.0000 0.0000 0.0000
Precision for the Gaussian observations 33.5108 2.0170 29.6989 33.4576 37.6385
Precision for TP 175.7980 118.2568 44.5864 145.6604 483.8306
Precision for SDD 799.4793 1218.8488 90.9053 446.3809 3687.8303
Precision for GDD 218.1584 157.0026 52.0294 176.4296 631.5433
Precision for RICH 194.8733 133.8561 48.4094 160.3397 543.9997
Precision for CNTY 9.0170 1.2649 6.7656 8.9370 11.7374
Phi for CNTY 0.3151 0.0847 0.1656 0.3099 0.4946
Precision for AERCODE.id 108.3525 46.5903 44.9076 99.3306 223.7402
Precision for AERCODE.id2 3392.9496 1388.9797 1465.2164 3134.1177 6805.8228
[[2]]

[[3]]
Table: Model Diagnostic Metrics
DIC CPO MSE R2
---------- --------- ---------- ----------
-348.8429 125.4483 0.0220956 0.8541136
[[1]]

[[2]]

[[1]]

[[1]]

[[1]]

[[1]]

Cross Validation Diagnostic Metrics
| 0.7977482 |
0.0715224 |
0.7522307 |
2 |

Climate Effects Summary for Midwest

Climate Effects Summary for South

Climate Effects Summary for Northeast

Climate Effects Summary for West
